NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Assessment and Optimization of 

Lidar Measurement Availability 

for Wind Turbine Control

Preprint

S. Davoust, A. Jehu, M. Bouillet, M. Bardon, 

and B. Vercherin

Avent Lidar Technology

A. Scholbrock, P. Fleming, and A. Wright

National Renewable Energy Laboratory

Presented at EWEA 2014

Barcelona, Spain

March 10–13, 2014

Conference Paper

NREL/CP-5000-61332

May 2014
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US 

Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications. 

Available electronically at http://www.osti.gov/scitech

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

 Printed on paper containing at least 50% wastepaper, including 10% post consumer waste. 
1 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Assessment and Optimization of Lidar Measurement 

Availability for Wind Turbine Control

Samuel Davoust1*, Alban Jehu1, Michael Bouillet1, Mathieu Bardon1, Benoist Vercherin1, 

Andrew Scholbrock2

, Paul Fleming2, and Alan Wright2 

1: Avent Lidar Technology, Orsay, France

2: National Renewable Energy Laboratory, Boulder, CO, USA

*Avent Lidar Technology, 3 rue Jean Rostand, 91400 Orsay, France; email: sdavoust@aventlidar.com

Abstract

Turbine-mounted lidars provide preview 

measurements of the incoming wind field. By 

reducing loads on critical components and 

increasing the potential power extracted from the 

wind, the performance of wind turbine controllers

can be improved [2]. As a result, integrating a light 

detection and ranging (lidar) system has the 

potential to lower the cost of wind energy. This 

paper presents an evaluation of turbine-mounted 

lidar availability. Availability is a metric which 

measures the proportion of time the lidar is 

producing controller-usable data, and is essential 

when a wind turbine controller relies on a lidar. To 

accomplish this, researchers from Avent Lidar 

Technology and the National Renewable Energy 

Laboratory first assessed and modeled the effect of 

extreme atmospheric events. This shows how a 

multirange lidar delivers measurements for a wide 

variety of conditions. Second, by using a theoretical 

approach and conducting an analysis of field 

feedback, we investigated the effects of the lidar 

setup on the wind turbine. This helps determine the

optimal lidar mounting position at the back of the 

nacelle, and establishes a relationship between 

availability, turbine rpm, and lidar sampling time. 

Lastly, we considered the role of the wind field 

reconstruction strategies and the turbine controller 

on the definition and performance of a lidar’s 

measurement availability. 

1. Introduction

Integrating light detection and ranging (lidar) 

technology to improve wind turbine controls is a 

potential breakthrough for reducing the cost of wind 

energy [1]. By providing undisturbed wind 

measurements up to 400 m in front of the rotor, 

turbine-mounted lidars provide an accurate update of 

the turbine inflow with a preview time of several 

seconds. Several studies have evaluated potential 

reductions in loads using integrated lidar, either by 

simulation [2, 3] or full-scale field testing [4, 5]. 

Leading wind turbine manufacturers have also 

started to evaluate the technology [6]. From a 

business standpoint, one of the key aspects that 

should be validated is the availability of lidar 

measurements; if the lidar is not delivering 

measurements, benefits cannot be obtained.

Figure 1: Schematic of lidar measurement process 

for wind turbine control.

The present work provides an overview of 

the measurement availability that can be achieved 

with a turbine-mounted lidar, and describes 

methods to optimize lidar integration for turbine 

control. Availability can be generally defined as the 

percentage of valid “wind estimations” obtained 

over a given period of time. The measurement 

process for a lidar has now been described in [7]. 
2 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

As depicted in Figure 1, the key parameters 

governing measurement availability with a turbinemounted lidar are the atmospheric conditions, the 

lidar configuration on the wind turbine, and the wind 

field estimation algorithm.

2. Problem

Lidar systems acquire signals along one or 

several lines of sight (beams). The measurement 

which is derived is a projected wind speed along the 

line-of-sight (LOS). The average availability of wind 

measurements along a line of sight can be defined 

as the ratio of valid LOS measurements over the 

total number of measurements expected within a 

specified period. In the following discussion, this 

parameter will be defined as ALOS. When the lidar 

beam propagates freely in the atmosphere, ALOS is 

a function of the type of lidar system used, the lidar 

measurement configuration (e.g., sampling rate and 

range), and of the atmosphere encountered.

As shown in Figure 1, the lidar mounting 

position on the turbine can affect the line-of-sight 

(LOS) measurement. The main impacts on the lidar 

measurement from the turbine can be a periodic 

lidar motion (e.g., oscillation or rotation), or a 

periodic blade passage in front of the lidar beams. 

These effects could impact the LOS availability so 

we denote it as ALOS

T in the turbine-mounted 

configuration. The difference between ALOS and 

ALOS

T may also depend on the type of lidar 

technology used, and it is expected that ALOS

T < 

ALOS. 

LOS measurements are reconstructed into 

an effective signal such as a rotor-averaged wind 

speed [8] and used by the turbine controller as 

feedforward input of the wind field. This leads to 

defining a validity rule with respect to the number of 

LOS measurements necessary (i.e., distance and 

time) to build this effective signal. This definition 

may depend on the type of measurements available 

(e.g., single range or multiple ranges). The choice 

of one definition thus results in an effective 

availability noted as AE that could be greater or less 

than ALOS or ALOS

T

. Design of the definition for a 

specific lidar and turbine controller technology may 

be required.

In this paper, the sensitivity of lidar 

availability from external parameters is investigated 

and modeled for each step. Operational feedback 

from a lidar system installed at the National 

Renewable Energy Laboratory (NREL) test site in 

Boulder, Colorado, and data collected from over 20

additional turbine-mounted lidars deployed in 

several configurations is used to verify the 

analyses. 

3. Effects of atmospheric 

conditions on availability

In regular atmospheric conditions, lidars are 

designed to deliver 100% LOS measurement 

availability across all specified ranges. However, 

under specific conditions, a lidar measurement can 

become unavailable at a specific range if the 

returned signal is not strong enough or does not 

match quality filters. Thus, verifying the availability 

of measurements within a specified measurement 

range is central to the control application because 

range determines the prediction time for the turbine. 

The carrier-to-noise ratio (CNR) is one of the main 

parameters that can be used to flag lidar wind 

measurements as valid [7].

3.1.Simulation of lidar signal in 

different atmospheric conditions

To understand the behavior of average 

CNR, we performed a theoretical computation of the 

lidar signal using an in-house lidar simulation tool 

that was previously validated [7]. The main 

parameters of the computation are the atmospheric 

transmission (α) and backscattering (β) coefficients 

on the one hand, and the choice of lidar 

components on the other. For this study, the 

parameters used in the simulation were set to the 

same parameter values that were used on a Wind 

Iris lidar that was mounted on a wind turbine at the 

National Wind Technology Center at NREL. The 

atmospheric backscatter coefficient was varied from 

10-3 (extreme fog) to 10-8 (higher than normal, or 

very high visibility) and a hypothesis was made for 

the transmission coefficient (α=βx40). The results 

are provided in Figure 2.

Figure 2 (a): Simulation of lidar system CNR in 

good visibility. The upper curve corresponds to 

“normal visibility”. The lower curve corresponds 

to a “very high visibility” case.
3 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 2 (b): Simulation of lidar system CNR in 

a fog situation. The upper curve corresponds to 

“Heavy Fog”. The lower curve corresponds to an 

“Extreme Fog” situation.

Under normal visibility, Figure 2(a) shows 

that the current setup should provide a slightly 

peaked CNR around 100 m. If visibility increases 

from being “normal” to “very high,” the concentration 

of aerosol particles, which reflect the lidar signal, 

decreases. As can be seen in Figure 2(a), this 

effect should cause CNR to globally decrease, 

which could lead to a reduced availability at close 

and long range. In foggy conditions, Figure 2(b) 

shows that the CNR curve should have a different 

shape, with a much stronger initial CNR signal, 

followed by a faster decrease. This effect can be 

explained by the fact that fog is a suspension of 

micro-water-droplets, thereby providing a stronger 

reflective signal at close range, but at the same time 

scattering the laser energy at greater distances, 

leading to a faster drop of CNR. In extreme fog, the 

concentration of water droplets is higher: as a result 

a stronger reflection and CNR should be observed 

at close range but as the light will diffuse quicker at 

longer range, thus the rate of CNR decrease might 

increase.

3.2. Observations and analysis of 

experimental data

We performed an analysis of lidar LOS

availability under various atmospheric conditions. 

Over the course of a 6-month campaign, a Wind Iris 

lidar was installed on the NREL two-bladed Controls 

Advanced Research Turbine (CART2) located in 

Boulder, Colorado, at 1,855 m altitude, as shown in 

Figure 1. This pulsed lidar measures 10 ranges at 2

Hz and its optics are focused around 100 m to 

ensure an optimal availability from 50 m to 150 m. 

For this lidar system, the min/max CNR rule was set 

to (-19 dB, +10 dB). Using a webcam located at 100

m from the turbine and other instrumentation, we 

created weather categories to investigate lidar 

availability in clear sky, heavy rain, snow, or fog.

Periods where the turbine was stopped were 

identified to compute ALOS, independently of 

lidar/turbine interactions.

Figure 3 provides examples that show the 

performance of a commercial lidar dedicated to

close range measurements (40 m to 200 m). Under 

nominal conditions (normal visibility), ALOS is 

constant and near 100%, but starts to decrease 

beyond 160 m. During the heavy rain event, 7 mm 

of precipitations was recorded in 10 minutes (30 

mm fell within 1 hour). ALOS is observed to be 

maintained, and even increased in the farthest 

ranges, suggesting that rain does not itself reduce 

availability. Degradation of availability is, however, 

observed during a snow event. Nevertheless, the 

CNR average is maintained around -15 dB at 

around 100 m, providing nearly 100% of availability 

at this range. In heavy fog (visibility below 50 m), 

CNR strongly increases at close range, but 

availability is lower at 50 m than at 200 m. The fact 

that availability is lower while the CNR is high is 

explained by a factory +10 dB CNR upper limit. It 

should be assessed whether this upper limit should 

be removed for turbine control applications.

The experimental CNR findings agree well 

with the simulations for good visibility and heavy fog 

cases and confirm that, with proper lidar system 

design, strategies could be developed to deliver 

measurements even in very difficult atmospheric 

conditions. However, Figure 3 also shows that the 

min/max CNR amplitude varies as a function of 

atmospheric conditions. As a result, we recommend 

investigating the statistical distribution of CNR when 

planning a field campaign. Indeed, even if the 

average CNR implies valid measurement, a large 

dispersion that brings some CNR values below the 

threshold could reduce availability.
4 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

10-minute average LOS availability 

ALOS (turbine stopped) as a function 

of range

10-minute average, min and max 

CNR as a function of range

Normal visibility27/06/2013 18:50 (UTC)

Heavy Rain12/09/2013 20:40 (UTC)

Snow22/04/2013 14:00 (UTC)

Heavy Fog 01/05/2013 16:40 (UTC)

Figure 3 : Atmospheric conditions experienced over a 10-minute period, field pictures, lidar system 

average availability ALOS, and 10-minute average, min, and max Carrier-to-Noise Ratio (CNR) as a 

function of range. The dashed black line reproduces the CNR simulated in Figure 2a (good visibility) 

and 2b (heavy fog) for reference.

4. Availability behavior according 

to lidar/turbine setup

In this section, we focus on the availability

impact of the lidar geometry and mounting position 

on the wind turbine. For instance, different mounting 

positions have an effect on availability because of

the interference between the lidar laser signal and 

the turbine’s blades. The lidar can be mounted at 

various heights on the turbine nacelle, such as just 

behind the rotor or at the back of the nacelle, as 

shown in Figure 4 (a). However, because the 

turbine’s blades can interfere with the lidar’s laser 

beams, special consideration is needed to ensure 
5 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

that blade passing does not affect the wind 

measurement. For a pulsed lidar, the signal 

reflected by a blade does not contribute to 

measurements because the time of flight for the 

return signal does not match that of upstream 

measurements, and is therefore rejected. Another 

possibility is to install the lidar system inside the 

turbine’s spinner [9], which has the advantage of 

removing the blade interference but may require a 

more complex mechanical integration, and data 

transfer and processing system. 

4.1.Evaluation of signal availability 

from average geometrical 

blocking

Figure 4 (a): Schematic representation of a lidar 

system at the back of a wind turbine’s nacelle.

Figure 4 (b): Schematic representation of the 

position of the lidar beam in the rotor plane.

The first parameter that should govern

turbine-mounted lidar measurement availability 

ALOS

T is the percentage of average available field of 

view allowed by the rotor, as noted by Δ. As shown 

in Figure 4 (b), it can be computed as the ratio 

between the sum of the three blades’ root diameter 

(3 dL) and the rotor circumference at the local 

radius where a lidar beam intersects with the blades

(2 π RL). This ratio is given by Eq. 1: 

∆= 1 − 3dL

2πRL

 (1)

Assuming a 2.5-m blade root diameter, Figures 5 

(a) and (b) show the evolution of Δ as a function of 

the lidar system distance behind the rotor, 

considering different lidar beam opening angles (α) 

and heights (h) above the rotor axis. Figure 5 (a) 

and (b) shows that bringing the lidar to the back of 

the nacelle and raising it above the hub both 

improve Δ. Similarly, a wider beam opening angle 

also improves availability, especially as the lidar is 

moved back on the nacelle. These theoretical

results are in line with field observations on over 20 

documented turbine lidar installations on various 

nacelle types from 1.5-MW to 7.5-MW turbines, 

where this parameter varied from 50% to 80%.

Figure 5 (a): Available field of view Δ as a function 

of lidar system distance behind the rotor for two 

heights above the rotor axis and a beam opening 

angle of 15°.

Figure 5 (b): Available field of view Δ as a function 

of lidar system distance behind the rotor for two 

heights above the rotor axis and a beam opening 

angle of 30°.
6 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4.2.Evolution of lidar signal 

availability with temporal 

blocking effect

Temporal parameters such as the lidar 

measurement integration time (σ) and the rotor 

rotation speed (rpm) also have an effect on ALOS

T

. 

Indeed, Figure 6 shows an example of the lidar 

signal availability along one beam as a function of 

rotor rpm speed. For low rpm, the figure shows that 

availability is near 65%, which is the valid field of 

view Δ, but reaches 100% when rpm is above 80% 

of turbine rated speed. This dependence with rotor 

rpm can be explained by the temporal nature of the 

measurement process. In fact, we show below that 

Δ sets the upper bound to measurement availability 

at a very low rpm, while the upper bound to 

measurement availability of a high rpm depends on 

a time scale ratio between the lidar signal 

acquisition time and the blade passage period.

ALOS

T

Normalized generator rpm

Figure 6: Example of Lidar beam availability 

as a function of normalized generator rpm [9].

For modern wind turbines, the rotor speed 

varies between 3 rpm and 18 rpm [10], while lidar 

signal integration time can vary from 1 Hz to 50 Hz 

[7]. For a low rotor speed (say below 5 rpm), the 

blade passage will tend to last longer than the lidar 

signal integration time. As a result, a blade can 

completely block a measurement. In this case, the 

percentage of data loss is equal to the percentage 

of time a blade is in front of a beam, which is 

exactly 1-Δ. 

However, for a faster speed (say above 10 

rpm), the blade passage time will tend to become

lower than the Lidar acquisition time. In this case, 

depending on the lidar technology used, the lidar 

measurement may still be valid despite the blade 

partially blocking the beam. The ratio between the 

lidar signal integration time (σ) and the time taken 

for a blade to pass completely in front of a beam 

can be written as:

Σ = σ rpm

60

3

1−∆ (2)

If this parameter becomes greater than one, 

it means the lidar can always “see through the 

blade” and completely gain back the availability lost 

(given by 1-Δ). Σ = 0 corresponds to the low rotor 

speed case where the Lidar availability is Δ as 

described above. If Σ is between 0 and 1, a part of 

the 1- Δ availability loss can be recovered. This

recovered part is governed by the amount of CNR 

decrease due to a partial obstruction. As CNR is 

measured as a decimal logarithm scale, when a 

blade obstruction divides the acquisition time by 2, 

this removes 3 dB of CNR. In operational 

conditions, the validity of a LOS measurement will 

be kept as long as after the CNR loss, the CNR 

remains above the threshold. 

As a first model, it can be roughly assumed 

that availability loss is inversely proportional to Σ. In 

this case, lidar signal availability should be 

estimated by: 

ALOS

T = �

∆ + Σ (1 − ∆) if Σ < 1

1 if Σ ≥ 1

 (3)

To evaluate this model, researchers 

investigated availability using average lidar data 

from 20 different turbine-mounted configurations. 

This evaluation allowed us to cover various lidar 

signal-sampling rates and turbine rpm 

configurations, as well as cases where the lidar was 

in the back or front of the nacelle. Furthermore, this

evaluation generates a wide range for parameters Σ 

and Δ, as shown in Figure 7 (a). 

Figure 7(a): Map of the lidar system field-ofview Δ and lidar-to-blade time ratio Σ observed 

over 20 different turbine-mounted lidar system 

configurations. 
7 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

ALOS

T

∆ + Σ (1 − ∆)

Figure 7(b): Corresponding correlation between 

measured lidar signal availability (red squares)

and estimated availability parameter according to 

eq. 3 (dashed line) as a function of Δ + Σ(1- Δ). 

In turn, Figure 7 (b) shows a good 

correlation between measured availability and 

estimated availability, thereby confirming the 

approach used with the above model with the 

existence of two regimes. When Σ is much smaller 

than 1, the lidar signal availability is governed by Δ, 

the available field of view. When Σ is larger than 1, 

the lidar signal availability should be 100%. This 

model should help determine which lidar 

configuration best suits measurement requirements.

A next step would be to refine the model according 

to realistic CNR distribution with range such as 

depicted in Figure 3, combined with a CNR loss 

which is dependent to blade obstruction time. 

5. Effect of real-time wind field 

reconstruction algorithm

We have shown that LOS signal availability 

can be modeled simply. Although this topic is still 

being investigated, it is worth noting that from a 

wind turbine control point of view, only 

reconstructed wind parameters estimated using 

several lidar beams or ranges should be used. For 

instance, using measurements that are available 

across all lidar measurement ranges can improve 

the estimation of wind parameters. The validity of 

wind estimation then depends on the minimum 

number of ranges and points required during a 

specific time interval, according to the wind turbine 

control requirements. Thus, the lidar system 

availability performance should be evaluated 

according to this metric. 

6. Conclusion

We investigated the measurement 

availability of turbine-mounted lidar systems for a 

wide range of parameters including lidar system 

characteristics, mounting configurations, 

atmospheric conditions, and wind reconstruction 

algorithms. We demonstrated that the main effects 

of these parameters can be modeled and predicted, 

and established several aspects that contribute to 

the definition and performance of lidar availability 

for wind turbine control. We obtained promising 

results in various atmospheric conditions.

In this paper, we provide a critical analysis 

of availability performance with respect to turbine 

control application, and propose optimization 

strategies to remedy cases of low performance. 

Further work and collaboration between research 

teams, and lidar and turbine manufacturers is 

required to validate this approach, which can then

strengthen the value behind integrating lidar 

systems into the next generation of turbines.

References

[1] A. Byrne, T. McCoy, K. Briggs, T. Rogers. 

“Expected Impacts on Cost of Energy through Lidar 

Based Wind Turbine Control.” European Wind 

Energy Association Conference, Copenhagen, 

Denmark, 2012.

[2] E. A. Bossanyi, A. Kumar, O. Hugues-Salas. 

“Wind turbine control applications of turbinemounted LIDAR.” Torque from Wind Conference, 

Oldenburg, Germany, 2012.

[3] C. L. Bottasso, F. P. Pietro Pizzinelli, C. E. D. 

Riboldi. “Lidar-enabled real-time control of wind 

turbines.” European Wind Energy Association

Conference, Vienna, Austria, 2013.

[4] D. Schlipf, P. Fleming, F. Haizmann, A. 

Scholbrock, M. Hofäß, A. Wright, P. W. Cheng. 

“Field Testing of Feed forward Collective Pitch 

Control on the CART2 Using a Nacelle-Based Lidar 

Scanner.” Torque from Wind conference, 

Oldenburg, Germany, 2012.

[5] A. Scholbrock, P. Fleming, L. Fingersh, A. 

Wright,D. Schlipf, F. Haizmann, F. Belen. “Field 

testing LIDAR based feed-forward controls on the 

NREL controls advanced research turbine.” 51st 

AIAA Aerospace Sciences Meeting Including the 

New Horizons Forum and Aerospace Exposition, 

Grapevine, Texas, 2013.

[6] A. Koerber, C. Mehendale. “Lidar Assisted 

Turbine Control...An Industrial Perspective.” 

American Wind Energy Association Conference, 

Chicago, USA, 2013.
8 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

[7] J. P. Cariou, M. Boquet. “LEOSPHERE Pulsed 

Lidar Principles.” UpWind WP6 report on Remote 

Sensing Devices. 2010.

[8] D. Schlipf, L. Y. Pao, and P. W. Cheng. 

“Comparison of Feed-Forward and Model Predictive 

Control of Wind Turbines Using LIDAR.” 

Proceedings of the Conference on Decision and 

Control, Maui, USA, 2012. 

[9] T. Mikkelsen, N. Angelou, K. H. Hansen, M. 

Sjöholm, M. Harris, C. Slinger, P. Hadley, R. 

Scullion, G. Ellis, G. Vives. “A spinner-integrated 

wind Lidar for enhanced wind turbine control.” Wind 

Energy, Vol. 16, 2013, p. 625–643.

[10] R. Wagner, S. Davoust. “Nacelle lidar for power 

curve measurement _ Avedøre campaign.” DTU 

Wind Energy E-0016, 2013.

[11] A. D. Hansen, F. Iov, F. Blaabjerg, L. Hansen. 

“Review of contemporary wind turbine concepts and 

their market penetration.” Wind Engineering, Vol. 

28, No. 3, 2004.